Paper Citation Record · LEDGER

OpenTinker: Separating Concerns in Agentic Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 5 of 5 outbound references and 6 inbound Pith citation observations for arXiv:2601.07376.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2601.07376 v2

Coverage vector

measured 5 of 5 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T11:08:09.510213Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T12:27:08.805309Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T07:56:47.922960Z

Reference resolution

5 of 5 outbound references displayed

verified exact0
verified fuzzy0
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 70d7ed3e-a225-46ab-9638-255e8247c743 · outbound

This paper cites AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning.

OpenTinker: Separating Concerns in Agentic Reinforcement Learning AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T11:08:09.185820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 842e9f74-e1b6-4333-ae05-237dacb7c4f8 · outbound

This paper cites OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework.

OpenTinker: Separating Concerns in Agentic Reinforcement Learning OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T11:08:09.266985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 163af5e1-8833-4886-9f02-b695ce74d492 · outbound

This paper cites Tinker, 2025.

OpenTinker: Separating Concerns in Agentic Reinforcement Learning Tinker, 2025

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T11:08:09.350328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa5821be-347f-433a-8119-8b008824ceb0 · outbound

This paper cites Agent Lightning: Train ANY AI Agents with Reinforcement Learning.

OpenTinker: Separating Concerns in Agentic Reinforcement Learning Agent Lightning: Train ANY AI Agents with Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T11:08:09.429860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec43eaac-95a9-4c57-aeb9-07ecefb38d4d · outbound

This paper cites Hybridflow: A flexible and efficient rlhf framework.

OpenTinker: Separating Concerns in Agentic Reinforcement Learning Hybridflow: A flexible and efficient rlhf framework

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T11:08:09.510213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 952ab6ce-a753-4394-acb0-ca502f3766bc · inbound

Agentic AI Systems Should Be Designed as Marginal Token Allocators cites this paper.

Agentic AI Systems Should Be Designed as Marginal Token Allocators OpenTinker: Separating Concerns in Agentic Reinforcement Learning

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-07-07T03:18:52.285192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a5b811a2-f57d-41b7-8258-3bd9938189e2 · inbound

MinT: Managed Infrastructure for Training and Serving Millions of LLMs cites this paper.

MinT: Managed Infrastructure for Training and Serving Millions of LLMs OpenTinker: Separating Concerns in Agentic Reinforcement Learning

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-07T03:18:52.285192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 899154d4-fdd6-40d0-890c-1af55d0dc941 · inbound

MinT: Managed Infrastructure for Training and Serving Millions of LLMs cites this paper.

MinT: Managed Infrastructure for Training and Serving Millions of LLMs OpenTinker: Separating Concerns in Agentic Reinforcement Learning

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-07T03:18:52.285192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 34186f12-8aa4-4978-9cdd-21d18e70a184 · inbound

AgentJet: A Distributed Swarm Training Framework for Agentic Reinforcement Learning cites this paper.

AgentJet: A Distributed Swarm Training Framework for Agentic Reinforcement Learning OpenTinker: Separating Concerns in Agentic Reinforcement Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-07T03:18:52.285192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f1932882-5d1f-4461-8d10-0ce09ed13d4d · inbound

AgentJet: A Distributed Swarm Training Framework for Agentic Reinforcement Learning cites this paper.

AgentJet: A Distributed Swarm Training Framework for Agentic Reinforcement Learning OpenTinker: Separating Concerns in Agentic Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T12:27:08.805309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91d2c092-7bfc-4e20-a5a3-0b80ad0888f9 · inbound

JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models cites this paper.

JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models OpenTinker: Separating Concerns in Agentic Reinforcement Learning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-01T21:32:08.591340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.